A methodology for propagating and constraining the uncertainty inherent in real-time flood forecasting is presented and demonstrated on an application to the River Severn, UK. The flood forecasting system is based on a cascade of rainfall-runoff and flood routing models, developed using stochastic transfer functions with state dependent parameterisations to allow for nonlinearity. The nonlinear...

Multistage stochastic programming is a key technology for making decisions over time in an uncertain environment. One of the promising areas in which this technology is implementable, is medium term planning of electricity production and trading where decision makers are typically faced with uncertain parameters (such as future demands and market prices) that can be described by stochastic proc...

In order to catch up with reality, all the macro-decisions related to long-term mining production planning must be made simultaneously and under uncertain conditions of determinant parameters. By taking advantage of the chance-constrained programming, this paper presents a stochastic model to create an optimal strategy for producing bimetallic deposit open-pit mines under certain and uncertain ...

Finding the location for plans like factories or warehousesfor any organization is an important and strategic decision. Costs oftransportation which are the main part of the price of the goods, is thefunction of the location of these projects. to find the optimum locationof these projects, there have been various methods proposed which areusually defined (not random). In reality and in dealing ...

This paper presents an online algorithm for mixture model-based clustering. Mixture modeling is the problem of identifying and modeling components in a given set of data. The online algorithm is based on unsupervised learning of finite Dirichlet mixtures and a stochastic approach for estimates updating. For the selection of the number of clusters, we use the minimum message length (MML) approac...

The semi-Markov decision process can be solved via reinforcement learning without generating its transition model. We briefly review the existing algorithms based on approximate policy iteration (API) for solving this problem for discounted and average reward under the infinite horizon. API techniques have attracted significant interest in the literature recently. We first present and analyze a...

In this paper, the problem of chaos, stability and estimation of unknown parameters of the stochastic lattice gas for prey-predator model with pair-approximation is studied. The result shows that this dynamical system exhibits an oscillatory behavior of the population densities of prey and predator. Using Liapunov stability technique, the estimators of the unknown probabilities are derived, and...